Taming The Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics (Wiley and SAS Business Series)

companies have completely taken analytic scalability to a new level with in-database analytics.

We’re talking about running analytics processes 40, 50, even 100 times faster than is possible with traditional approaches.

Let’s assume an organization has decided to pursue text analysis against customer service e-mails and social media commentary. What are the most critical problems to solve? Is understanding the general sentiment of the comments the priority? Is identifying specific individuals who need attention the priority? Is identifying trends in terms of how o

Recently, with increases in computing power and the rise of in-database processing, telecom companies started to seriously explore the network of contacts each customer has.

Many innovations, while highly impactful by themselves, are even more compelling when the later innovations they enabled are examined. The second principle is the ripple effect.

Such analysis is not only interesting from a social dynamics perspective, but it’s also being used to increase the effectiveness of customer retention and recruitment.

The use of text analysis against customer service dialogues is only beginning to impact a wide range of industries. Neither the data, the tools to analyze it, nor the scalable systems required were widely available until recently.

One of those innovations is the use of in-database processing to enable scalable analytics, as discussed in Chapter 4.

team will have to focus on analytic innovation if they are using in-database analytics and MapReduce so that their processes run dozens of times faster.